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theoretic terms, one speaks of probability, noise, and divergence. A stochastic system is probabilistic, but may still very well be a continuous stochastic system, that is, have an information dimension of one, and ultimately represent the same amount of information as a chaotic system. Indeed, one can have a stochastic chaotic system, with an arbitrary rate infromation decay/divergence. Chaotic and stochastic are orthogonal classifications. A linear(non-chaotic) and non-stochastic system simply evades the question of information and therefore predictability altogether. We must therefore disclude it from any concept of predictability or randomness. Indeed, randomness already discludes all non-stochastic systems. Where a system is "random", it has "divergence", times a scalar. A system may have divergence distributed evenly in resepct to a real-valued parameter. In this case, one simply adds a "noise" term: + N. This is so-called "randomness". In a chaotic system, on the other hand, the divergence is concentrated by a point. Regardless, one has convergence and divergence, and in either case noise is noise is noise. i.e. randomness is divergence is noise. In terms of "causuality" in the markov sense, there is really no fundamental discriminatory factor. It still remains possible, ofcourse, for one to make a (superficial) mathematical distinction, but one should not look for any ontological distinction - it is ontologically unnecessary for there to be a distinction.
310:, inextricably, noise, and thus noise, since it cannot be reduced, replaced, or removed, must ultimatly be in-itself, regardless of whether it is "extrinsic" or "intrinsic". (The question was never what name we give to the difference of the being-of-noise, but rather the simply differential character of the difference itself.) The more fundamental question is not intrinisic vs. extrinsic, but in-itself (essential) vs. phenomenological. But the two positions are informationally equaivalent/indistinguishable: one nonetheless has "noise", "information", and everything else. There is no way to statistically distingush among the consequences beyond a mere quantification of the "noise rate". --
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A chaotic is neccessarily a non-equilibrium system; chaotic systems only exist where there is a flow of energy, and thus also neccessarily, a flow of information. The "unpredictability" of a chaotic system is part of this flow, just as the "noise" in a stochastic system is an influx of information.
284:, operates on un-computable numbers. This means that even a theoretically ("in principle") "perfect" computer could not compute the vast majority of trajectories. And by vast majority, I mean 99.999999999...%. For a formal definition of computability, see Turing's essay "On Computable Numbers".
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These two graphs have to be some of the worst I've ever seen. They are so badly labelled, and give no idea to a reasonable user as to what the they are showing (the right graph specifically). "Parameter" should at least be labelled "parameter, r", and none of t, T, f and k are anywhere defined
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I was going to fix this paragraph myself, and I had much trouble, because when ever I encountered a "not the same as..." I thought for a minute and eventually answered "Yes it is." The proper way to speak of predictability/unpredictability is in information-theoretic terms. In information
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The first graph on the page (the animation) has mislabeled axes. The horizontal axis should be "n" to be consistent with the equation given in the article. Took me forever to figure out what it was talking about. I'd do it myself but I don't know how to make animated gifs.
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properly. And " t=nT" is not how you properly label an axis. First, either it is "t" or it is "n" (and not "" either), and secondly, some description would be nice. I've seen better graphs than this on vixra. It needs to be sorted out or removed completely, unfortunately.
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Yes. If you iterate practically any function that's not linear (not a straight line), then there will be some starting values that give chaotic (although, of course, perfectly deterministic) results. The FractInt software comes with lots of them. -- DavidCary
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There are methods for proving that period 3 begins at exactly r = 1 + Sqrt(8). I can provide a published proof if necessary, but also did the proof myself as a presentation in a chaos theory class. I think this section could benefit by mentioning this.
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But I have not yet been complete enough, one might still raise the objection that a chaotic system is fully specified by a finite set of symbols and is non-stochastic. However, my point is that this is completely irrelevant. The symbols are arbitrary.
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But there is also the philosophical problem of "noise" as extrinsic "unknown" (determinism) - the scientific deterministic assumption - vs. noise as an in-itself (free-will). But in either case there
326:=1). For example if r was 4, you wouldn't get much chaotic behavior if you started with .75 .Is the talk about period and chaotic behavior talking about a general seed such as a transcendental one?
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tracing the first few iterates in order to give a visual explanation of the behavior of the chaotic "shadows". Not sure where/if it's worth publishing it... so I just mention it here, for now.
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07:41, 23 May 2007 (UTC) Please give a reference to published proof in article and if I'm not wrong good place for a your proof is wikibooks ( with link to wikibooks in article). --
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I.e., you have the
Mandelbrot set and the logistic map being a degree 2 polynomial. Are there any interesting examples with degree 3? Degree 4? Other types of functions?
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It would be interesting to read about connections to biology. Are there interesting observations in nature predicted by this? (Most interesting seems the range 3-3.45)
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I can't follow the algebra here. Mandelbrot is z = z^2 + c and logistic is x = r x (1-x). I don't think the formula given above converts these formulas into each other.
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The right limit for the t axis is too big. I suggest making it (0,75) instead of (0,200). It's really too difficult to see the transient dynamics in the current GIF.
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The population goes negative, which doesn't really make sense as a population model? I imagine that's why nobody seems to have bothered to consider this case.
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I could upload the
Mathematica .nb file. The algorithm is similar to the one described in the old image. Where in commons would I put the info? --
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XMIN : 0 XMAX : 1 XSCL : .1 YMIN : 0 YMAX : 1 YSCL : .1 XINIT : $ A2/.5+ WEBSTART : 0 WEBSTOP : 50 CURRENT : 1'-'*$ B1+2** REFERENCE : '
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When you have finished reviewing my changes, you may follow the instructions on the template below to fix any issues with the URLs.
318:"With r between 3 and 1+√6 (approximately 3.45)..." It seems like there's a typo in the 1+6, but I'm not sure what it should be --
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The difference is simply whether the distribution of this influx - this flow - is flat with respect to a given measurement.
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I created a higher resolution orbit diagram for the logistic map and was wondering if indeed it is worth putting here.
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One of the quantities X sub n on the right side of the equation should be an X sub 0. I'm not sure which one, though.
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Why does it keep saying independent of inital seed? It seems like (r-1)/r is a solution no matter what r is (r: -->
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Yes, that's correct. There are also explicit solutions for r = 2 and r = -2 More information can be found here:
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Thanks Matt, glad you like it. I scaled it down a bit and put it in. - Regards
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Should there be description on what happens if the value of r is below 0? --
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https://web.archive.org/web/20070415033246/http://lectures.nsitlounge.in/
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could you put the code ( in commons) and describe algorithm ? --
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for additional information. I made the following changes:
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Much better. I love it! Can you add it to the main page
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Chaos (or period 3) begins at precisely r = 1 + Sqrt(8)
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if r = 4, the logistic map appears to be solvable: set
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Are there any other formulae that have this property.
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403:{\displaystyle \,\!\phi =\cos ^{-1}(1-2x_{0})}
256:(Should this answer go on the article page ?)
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